Over the last three years, the crypto space has undergone massive upheavals. Alongside the boosting from stimulus packages in 2021, venture capital (VC) firms had invested $33 billion in crypto and blockchain startups.

The following year, the Federal Reserve triggered a domino of crypto bankruptcies with its interest rate hiking cycle, starting from the Terra (LUNA) crash and culminating in the FTX Ponzi scheme collapse.

The promise of DeFi lost its luster, not helped by over $3 billion lost in DeFi hacks during 2023. The ongoing Bitcoin bull run shows the lack of altcoin confidence as the so-called Altcoin Season is yet to manifest.

In June 2023, BlackRock’s head of strategic partnerships, Joseph Chalom, noted that DeFi’s institutional adoption is “many, many, many years away”. However, there is a case to be made that the emerging AI narrative can fuse with blockchain technology and its applications.

Taking in lessons from the previous cycle, what would that AI-crypto landscape look like?

Laying the AI Foundation with Crypto Composability

Looking back, it is safe to say that “DeFi” was subsumed by companies on top of tokenized layers, such as Celsius Network or BlockFi, rendering DeFi into CeFi. These companies successfully drove crypto adoption as such, only to end up sullying the very word “crypto”.

A renewed DeFi v2 should then focus on a superior user experience that doesn’t spark the demand for centralized companies to make it so. Most importantly, DeFi security must be fortified. The most promising solution in that direction is the zero-knowledge Ethereum Virtual Machine – zkEVM.

By abstracting chain transactions via zero-knowledge proofs (ZKPs), zkEVM increases network throughput and reduces gas costs. On top of that, zkEVM simplifies the user experience by facilitating alternative token payments for gas fees. In other words, zkEVM-like solutions pave the road to scalability needed for AI applications.

AI applications inherently involve high volumes of data, making it a potential bottleneck for blockchain networks. With this obstacle ahead, Polygon zkEVM makes it possible to generate AI artwork via the Midjourney image generator. In this process, the results could be tokenized as NFTs with low fees.

Building further on smart contracts of other kinds, the crypto space has laid the groundwork for AI with composability and permissionless access. Combined, this creates an autonomous and efficient infrastructure for financial markets. As every piece of market action can be disassembled into smart contracts, composability brings innovation across three composability layers:

  • Morphological – components communicating between DeFi protocols, creating new meta-features.
  • Atomic – ability for each smart contract to function independently or in conjunction with other protocols’ smart contracts.
  • Syntactic – ability for protocols to communicate based on standardized protocols. 

In practice, this translates to Lego DeFi bricks. For instance, Compound (COMP) allows users to supply liquidity into smart contract pools. This is one of DeFi’s revolutionary pillars as users no longer require someone’s permission to either loan or borrow. With smart contracts acting as liquidity pools, borrowers can tap into them by providing collateral. 

Liquidity providers gain cTokens in return as interest. If the supplied token is USDC, the yielding one will be cUSDC. However these tokens can be integrated across the DeFi board into all protocols compatible with the ERC-20 standard.

In other words, composability creates opportunities for the multiplicity of yields, so that no smart contract is left idle. The problem is, how to efficiently handle this rise in complexity? This is where AI comes into play.

Amplifying Efficiency with AI

When thinking of artificial intelligence (AI), the main feature that comes to mind is superhuman processing. Financial markets have long ago become too complex for human minds to handle. Instead, humans have come to rely on predictive algorithms, automation and personalization.

In TradFi, this typically translates to robo advisors prompting users on their needs and risk tolerances. A robo advisor would then generate a profile to manage the user’s portfolio. In the blockchain composability arena, such AI algorithms would gain much greater flexibility to siphon yields.

By reading the market conditions on the fly as they access transparent smart contracts, AI agents have the potential to reduce market inefficiencies, reduce human error, and increase market coordination. The latter already exists in the form of automated market makers (AMMs) that deliver asset price discovery.

By analyzing order flows, liquidity and volatility in real-time, AI agents are ideally suited to optimize liquidity supply and even prevent DeFi flash loan exploits by coordinating between DeFi platforms and limiting transaction sizes. 

Inevitably, as AI agents increase market efficiency through real-time market…



Source link